The Infrastructure Debt Every AI Team Eventually Pays

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Innoviti processes over ₹80,000 crore annually for 50,000+ merchants across India. Supporting enterprise retailers like Reliance Retail, Shoppers Stop, and DMart requires managing thousands of physical point of sale (PoS) EDC terminals across 2,000 cities. Operating this massive hardware footprint demands continuous field maintenance to prevent transaction downtime, and terminal downtime immediately stops merchant revenue.
Performance can be digitally validated. Governance and brand cannot. They require visual verification. To close each ticket, agents submit a complex mix of diagnostic logs, handwritten notes, store signboard photos, selfies, product sticker images, accessory shots, and the manually filled, stamped job sheet. Triangulating these visual proofs against the digital signals in real time, while catching both false positives and false negatives, is what drives ROI from the entire support investment.
Manual triangulation of these proof-of-work artefacts created a severe operational bottleneck. A large quality assurance team had to evaluate multiple transaction diagnostics and multiple photo images for every single ticket, and they could not move fast enough to match daily retail volumes. Innoviti was processing more than 7,000 service ticket logs every day, and the queue kept growing.
Innoviti needed a system that could extract data from these unstructured artefacts, verify completed work across all three dimensions, and scale with their operations.
Innoviti began with generative AI experiments on a general purpose cloud provider. The early tests confirmed that AI could review and validate unstructured field data, but moving from prototype to production exposed the limitations of shared API ecosystems.
The shared environments operated as a complete black box. They denied Innoviti the root level infrastructure control required to fine tune the model, troubleshoot errors, manage memory utilization, and guarantee the sub 30 second latency that real time field operations demand. Beyond performance, the variable token economics of general purpose cloud platforms made scaling financially unviable. There was a separate compliance dimension to address as well, given the payment data involved.
Innoviti developed a specialized AI engine designed to function as a structured audit layer for its distributed retail network. The system interprets the complex, often messy data generated during field interventions and converts it into verifiable operational records.
The architecture consists of two primary components:
The technical foundation is a custom Qwen 3.0 VL model, fine-tuned to recognize the specific technical vocabulary, hardware error codes, store signboards, product stickers, accessories, stamped job sheets, and maintenance patterns unique to the retail payment industry.
To transition these services from experimental code to a reliable production environment, Innoviti migrated its workloads to a dedicated inference stack on Neysa Velocis.
“You cannot run a real time service operations setup on infrastructure you cannot see. General purpose cloud APIs kept us locked out of the serving stack, making it impossible to diagnose latency spikes, manage cost and truly control our model’s behaviour.”
– Girish Varadarajan, Chief Data and AI Officer, Innoviti Technologies Ltd
The partnership focused on architecting a low-latency inference layer on Neysa Velocis to support the high throughput requirements of the Qwen 3.0 VL model. This allowed Innoviti to move their complex multimodal workflows, analyzing both physical hardware photos and technical text logs, into a secure, high-concurrency environment running 50 parallel LLM inferencing requests.
Neysa’s engineering team worked alongside Innoviti to harden the stack for production:
“Neysa provided the goldilocks zone we were looking for. They gave us the exact balance of root level control, dedicated compute, and hands on engineering support to get our system ready for production.”
– Girish Varadarajan, Chief Data and AI Officer, Innoviti Technologies Ltd
The migration to Neysa Velocis transformed TIFIN’s infrastructure from an operational constraint into a commercial advantage.
A dedicated resource model removed the scalability penalty of variable token pricing, and automating verification freed manual review teams for higher-value customer support work.
Production-scale AI sustained across 50,000+ merchant stores in 2,000 cities, absorbing the daily maintenance surge without backlog.
Vision-heavy field proofs and technical logs process fast enough to close tickets while agents are still on site, not after they’ve moved on.
Audit quality that matches or exceeds manual human review across all three dimensions of job completion – performance, governance, and brand.
Ready to move your enterprise AI use cases to production? Contact our team today to request a technical demonstration and explore our managed inference solutions.